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Record W2146209495 · doi:10.3968/7683

Discussion on the Undifferentiated Bombing of Hunan From Japanese Invading Army

2015· article· en· W2146209495 on OpenAlexvenueno aff
Guang-Hui Wu

Bibliographic record

VenueCanadian social science · 2015
Typearticle
Languageen
FieldEngineering
TopicMilitary Defense Systems Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsVictoryStrategic bombingPoliticsDeterrence theoryModernization theoryPolitical scienceScale (ratio)Spanish Civil WarWorld War IIHistoryLawGeographyCartography

Abstract

fetched live from OpenAlex

After the war broke out, based on the specific geographical location of Hunan and the implementation of the strategy “of political attack and long-lasting war”, Japanese invading army carried out large-scale undifferentiated bombings in Hunan. Japanese invading army’s undifferentiated bombings have the characteristics of long lasting in time, dense in scale, widely in geography with focus and they are strategic and of deterrence. In the eight years from the outbreak of the war for the victory of the war, the Japanese invading army’s undifferentiated bombing in Hunan has caused serious personal injury and property damage, and greatly hindered and undermined the process of modernization in Hunan.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.235
Threshold uncertainty score0.468

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0140.005
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0060.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.024
GPT teacher head0.215
Teacher spread0.192 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2015
Admission routes1
Has abstractyes

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